[MUSIC]. 
Hi Everyone. 
My name is Cecilia Aragon and I'll be 
your guest lecturer this week. 
We're going to be talking about basic 
principles of information visualization. 
This is in my opinion one of the most 
important interesting and fun components 
of the data science pipeline. 
We're going to be talking about how you 
take the data that you've collected and 
analyzed, and then actually present it in 
a form that makes sense to the human 
brain. 
I'm a professor here at the University of 
Washington in the Department of Human 
Centered Design and Engineering. 
And it'll be my pleasure to introduce you 
to this exciting subject. 
My research interests lie in the visual 
presentation of very large, scientific 
and social datasets. 
So, let's start with a few definitions. 
The word visualization is extremely 
overloaded. 
There are many possible meanings to the 
term so what are we talking about when we 
say we're studying Information 
Visualization? 
Well we want to consider the visual 
representation of information. 
The goals of Information Visualization 
include the effective communication of 
information or data, with clarity that 
means that the information should be 
presented in a way that's easy to 
understand by a human. 
With integrity, you present all the data 
and nothing but the data. 
And you want to stimulate viewer 
engagement with the data. 
Our focus in this course will be on 
effectiveness. 
We're talking about the clear and 
effective presentation of data to the 
human mind. 
Data visualization usually falls into 
three academic categories. 
Information visualization, scientific 
visualization and visual analytics. 
Information visualization typically 
involves abstract information that may 
not have a position in space. 
For examlpe, on the slide you can see a 
graph of stock prices around the year 
2000. 
We graphed the price, the dollar amount 
on the y-axis and time on the x axis. 
Scientific visualization usually involves 
displaying data that has a physical 
position in space. 
For example, here you see a methane 
combustion data set, where each of the 
data points has a 3D location. 
Visual analytics is a combination of 
highly interactive visual interfaces and 
sophisticated statistical learning 
algorithms. 
In this course we're going to be talking 
about basic principles of information 
visualizations. 
We're not going to be talking about 
scientific visualization specifically, 
nor about visual analytics, nor about the 
artistic field of visual design. 
The principles, however, of information 
visualization that we are discussing are 
applicable to all three domains. 
We will be discussing specifically this 
week how to map data variables to visual 
attributes. 
So, why is visualization important? 
Say you have a million numbers. 
What's the best way to communicate that 
information to the human brain? 
Well, imagine a table of a million 
numbers, it seems almost too vast to 
comprehend, much less define meaningful 
trends or information about it. 
But on the other hand, if you consider 
the number of pixels on your computer 
screen it's typically around a million 
and you have no trouble understanding it 
very quickly. 
And being able to, to act on the 
information you [UNKNOWN], so the human 
visual system is the highest-bandwith 
channel to the human brain. 
And it's the most efficient way to 
present large amounts of information so 
they make sense. 
Lets talk about a few examples, so may be 
the first use of visualization 
historically was for eliciting knowledge 
from data, let's suppose you have a table 
of numbers. 
We're, we'll start with a relatively 
small table with only 50 rows, one for 
each state in the union, three columns in 
each row. 
The name of the state, the percentage of 
college graduates in that state, and the 
per capita income. 
So you might have questions about this 
data, such as, which state has the 
highest income? 
Is there a relationship between income 
and education? 
Finally are there any outliers? 
Are there any trends? 
Well obviously all the data is present, 
and you could take your time looking at 
this data. 
You could identify the highest and lowest 
numbers. 
You could answer all these questions from 
the data. 
Everything is presented here. 
But is this the most efficient way to 
understand this data? 
Here's another way of presenting the data 
in a scatter plot. 
We have per capita income on the x axis 
and the percentage of college degrees on 
the y axis. 
And, as you can see, the question of 
which state has the highest income is 
instantly answered. 
We can see that Connecticut has the 
highest per capita income. 
Similarly, it's clear there's a trend 
that there's a correlation between per 
capita income and the percentage of 
college of degrees and there is a couple 
of interesting outliers. 
Nevada has a relatively high per capita 
income but a very low percentage of 
college graduates. 
So, all the information is presented in 
both of these forms but the, the visual 
representation is, is much more efficient 
at presenting meaning to the human mind. 
Well, so, okay, the first question that 
might come up is, well, yeah, but all 
this information could more easily have 
been accomplished by analyzing the data 
with a statistical program. 
You could find the maximum. 
You can find the minimum. 
You can find the regression line. 
However, it turns out the human brain is 
one of the best pattern matching tools 
that we have. 
Actually, it's, we're, the human brain's 
so far is still better at detecting 
patterns in data than any of our most 
sophisticated machine learning 
algorithms. 
So an immediate question that arises is. 
Why not just use a statistical program 
and identify the minimum, the maximum, 
the trend lines and go from there? 
Why bother with a visual representation? 
The answer is simply, the graphs reveal 
data that statistics may not. 
So, here's a famous example, Anscombe's 
Quartet. 
Here you have four small data sets that 
are quite different, as you can see by 
looking at them, but they have identical 
linear models. 
That is, the identical means and the 
identical trend lines. 
Again, you could just present the data 
in, in tabular form, but see how much 
more information we can learn about 
these, these individual data sets, just 
by looking at the data. 
So even though, we have identical means, 
identical regression lines. 
These data sets are quite different. 
So, so statistics may not tell us 
everything about a particular data set. 
I hope I've managed to wet your appetite 
for this coming weeks set of slides. 
I'm going to stop here and we'll continue 
on with more examples in the next 
lecture.